Computers, Vol. 15, Pages 136: COVID-19 Mortality, Human Development, and Age Across the WHO Member States: A Longitudinal Multilevel Count Data Analysis

Computers doi: 10.3390/computers15020136

Authors:
José Clemente Jacinto Ferreira
Ana Paula Matias Gama
Luiz Paulo Fávero
Ricardo Goulart Serra
Patrícia Belfiore
Igor Pinheiro de Araújo Costa
Miguel Ângelo Lellis Moreira
Marcos dos Santos
Wilson Tarantin Junior

This study aims to verify whether there is a statistically significant relationship between COVID-19 mortality rates, the Human Development Index (HDI), and population age across the World Health Organisation (WHO) member states. Despite the extensive literature on COVID-19 mortality and socio-demographic indicators, few studies explicitly integrate count data diagnostics, zero-inflation mechanisms, and multilevel longitudinal modelling to jointly capture cross-country heterogeneity and temporal dynamics. This study addresses this gap by applying a structured modelling framework that combines negative binomial, zero-inflated, and multilevel regression models to the WHO country-level data. For this purpose, two different statistical techniques were applied, namely: negative binomial regression modelling, zero-inflated negative binomial type for daily temporal exposure on 20 July 2020 and 20 July 2022, before and after the application of the first dose of the COVID-19 vaccine; and multilevel regression for two-level repeated measures data. Negative binomial regression estimates indicate statistically significant positive associations between HDI, age, and COVID-19 mortality rates before the application of the first dose of the vaccine. The variance decomposition from the definition of an unconditional model indicates significant variability in the occurrences of infection and death and between countries/states over time.

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José Clemente Jacinto Ferreira www.mdpi.com